Tensile Strength Prediction of Empty Palm Oil Bunch Fiber Composite with Artificial Neural Network
As the leading global producer of palm oil, Indonesia encounters substantial environmental challenges arising from the waste generated by empty palm oil fruit bunches (EPOFB). This research aims to develop an accurate Artificial Neural Network (ANN) model to predict the tensile strength of EPOFB fib...
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| Format: | Article |
| Language: | English |
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University of Muhammadiyah Malang
2024-11-01
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| Series: | JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering) |
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| Online Access: | https://ejournal.umm.ac.id/index.php/JEMMME/article/view/35619 |
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| author | Hery Tri Waloyo Agus Mujianto Richie Feriyanto |
| author_facet | Hery Tri Waloyo Agus Mujianto Richie Feriyanto |
| author_sort | Hery Tri Waloyo |
| collection | DOAJ |
| description | As the leading global producer of palm oil, Indonesia encounters substantial environmental challenges arising from the waste generated by empty palm oil fruit bunches (EPOFB). This research aims to develop an accurate Artificial Neural Network (ANN) model to predict the tensile strength of EPOFB fiber-reinforced composites. The method involves two types of ANN, namely Radial Basis Function (RBF) and Backpropagation, with testing using variations in immersion time, volume fraction, and length of EPOFB fibers. The research results show that both ANN models can predict tensile strength with a Mean Absolute Error (MAE) below 10%. However, the Backpropagation ANN shows superior performance with a training MAE of 0.0078 and a testing MAE of 0.45, compared to the RBF ANN, which has a training MAE of 0.371 and a testing MAE of 0.53. In conclusion, ANN Backpropagation is superior in prediction accuracy and characterization efficiency of EFB fiber-reinforced composites, offering an economical solution and supporting sustainable palm oil waste management. |
| format | Article |
| id | doaj-art-b9e11cbf03a14d02bd0191ec63830857 |
| institution | Kabale University |
| issn | 2541-6332 2548-4281 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | University of Muhammadiyah Malang |
| record_format | Article |
| series | JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering) |
| spelling | doaj-art-b9e11cbf03a14d02bd0191ec638308572025-01-21T05:02:28ZengUniversity of Muhammadiyah MalangJEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering)2541-63322548-42812024-11-0192778410.22219/jemmme.v9i2.3561933549Tensile Strength Prediction of Empty Palm Oil Bunch Fiber Composite with Artificial Neural NetworkHery Tri Waloyo0Agus Mujianto1Richie Feriyanto2Universitas Muhammadiyah Kalimantan TimueUniversitas Muhammadiyah Kalimantan TimurPoliteknik Negeri SamarindaAs the leading global producer of palm oil, Indonesia encounters substantial environmental challenges arising from the waste generated by empty palm oil fruit bunches (EPOFB). This research aims to develop an accurate Artificial Neural Network (ANN) model to predict the tensile strength of EPOFB fiber-reinforced composites. The method involves two types of ANN, namely Radial Basis Function (RBF) and Backpropagation, with testing using variations in immersion time, volume fraction, and length of EPOFB fibers. The research results show that both ANN models can predict tensile strength with a Mean Absolute Error (MAE) below 10%. However, the Backpropagation ANN shows superior performance with a training MAE of 0.0078 and a testing MAE of 0.45, compared to the RBF ANN, which has a training MAE of 0.371 and a testing MAE of 0.53. In conclusion, ANN Backpropagation is superior in prediction accuracy and characterization efficiency of EFB fiber-reinforced composites, offering an economical solution and supporting sustainable palm oil waste management.https://ejournal.umm.ac.id/index.php/JEMMME/article/view/35619artificial neural network (ann)backpropagationpalm oil empty bunches (poeb)radial basis function (rbf) |
| spellingShingle | Hery Tri Waloyo Agus Mujianto Richie Feriyanto Tensile Strength Prediction of Empty Palm Oil Bunch Fiber Composite with Artificial Neural Network JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering) artificial neural network (ann) backpropagation palm oil empty bunches (poeb) radial basis function (rbf) |
| title | Tensile Strength Prediction of Empty Palm Oil Bunch Fiber Composite with Artificial Neural Network |
| title_full | Tensile Strength Prediction of Empty Palm Oil Bunch Fiber Composite with Artificial Neural Network |
| title_fullStr | Tensile Strength Prediction of Empty Palm Oil Bunch Fiber Composite with Artificial Neural Network |
| title_full_unstemmed | Tensile Strength Prediction of Empty Palm Oil Bunch Fiber Composite with Artificial Neural Network |
| title_short | Tensile Strength Prediction of Empty Palm Oil Bunch Fiber Composite with Artificial Neural Network |
| title_sort | tensile strength prediction of empty palm oil bunch fiber composite with artificial neural network |
| topic | artificial neural network (ann) backpropagation palm oil empty bunches (poeb) radial basis function (rbf) |
| url | https://ejournal.umm.ac.id/index.php/JEMMME/article/view/35619 |
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